Mengyang Zhang
Shandong University, Jiangsu University, Shandong Normal University
Papers
16
Total Citations
260
H-Index
9
About
Mengyang Zhang is a prominent robotics researcher whose work sits at the intersection of autonomous navigation, semantic understanding, and intelligent service robotics. Over the past decade, Zhang has made significant contributions to the field of mobile robot object search, developing innovative frameworks that enable robots to efficiently locate and interact with objects in dynamic home environments. His research leverages metric-topological mapping, deep reinforcement learning, and semantic knowledge bases to address one of robotics' most persistent challenges: reliable, long-term autonomy in unstructured real-world settings. Among his most influential contributions is a suite of knowledge-driven object search methods, including metric-topological map construction and hierarchical semantic knowledge frameworks, which collectively demonstrate how human-like reasoning can dramatically improve robotic efficiency. His deep Q-learning approach to active object detection (34 citations) and semantic grounding scheme for dynamic environments (27 citations) reflect a consistent commitment to bridging perception, planning, and execution. Notably, Zhang's research portfolio also extends beyond software solutions, with recent work on coiled conductive polymer-based artificial muscles (30 citations) showcasing his versatility across hardware innovation. With over 200 cumulative citations, Zhang's body of work represents a foundational contribution to the next generation of intelligent home service robots.
Research Focus
Key Achievements
Top Papers
- 1Building Metric-Topological Map to Efficient Object Search for Mobile Robot37 citations · 2021
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- 5Artificial Muscles Based on Coiled Conductive Polymer Yarns30 citations · 2024
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